This paper proposed a novel method for transfer learning in Bayesian hyperparameter optimization based on the theory that the distributions of previously observed datasets contain significant information that should not be ignored during hyperparameter optimization on a new dataset. They propose solutions to compare different datasets through distribution estimation and then combine this information with the classical Bayesian hyperparameter optimization setup. Experiments show that the method outperforms selected baselines. Originality: the method is novel, although it mostly bridges ideas from various fields. Quality: I would like to congratulate the authors on a very well written paper.

U.S. safety investigators want to know why Tesla didn't file recall documents when it updated Autopilot software to better identify parked emergency vehicles, escalating a simmering clash between the automaker and regulators. In a letter to Tesla, the National Highway Traffic Safety Administration told the electric car maker Tuesday that it must recall vehicles if an over-the-internet update deals with a safety defect. "Any manufacturer issuing an over-the-air update that mitigates a defect that poses an unreasonable risk to motor vehicle safety is required to timely file an accompanying recall notice to NHTSA," the agency said in a letter to Eddie Gates, Tesla's director of field quality. The agency also ordered Tesla to provide information about its "Full Self-Driving" software that's being tested on public roads with some owners. The latest clash is another sign of escalating tensions between Tesla and the agency that regulates vehicle safety and partially automated driving systems.